12 MDCT Strategies for the Non-Invasive Work-Up of the Indeterminate Pulmonary Nodule

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12 MDCT Strategies for the Non-Invasive Work-Up of the Indeterminate Pulmonary Nodule

P. Herzog, M. Das, M. F. Reiser

P. Herzog, MD

Institute of Clinical Radiology, University of Munich, Klinikum Grosshadern, Marchioninistrasse 15, 81377 Munich, Germany M. F. Reiser, MD

Institute of Clinical Radiology, University of Munich, Klinikum Grosshadern, Marchioninistrasse 15, 81377 Munich, Germany M. Das, MD

Department of Radiology, University Hospital, RWTH Aachen, Pauwelsstrasse 30, 52074 Aachen, Germany

CONTENTS

12.1 Introduction 175

12.2 Classic Procedure for Work-Up 175 12.3 Advanced MDCT Strategies 177 12.4 Contrast Uptake 181

12.5 CT Volumetry 181

References 183

12.1 Introduction

Pulmonary nodules are one of the most common fi ndings on chest radiographs and CT scans of the thorax (Schoepf et al. 1999, 2001). As an incidental fi nding they often cause a cascade of diagnostic procedures including biopsy or surgery which, in many cases, is neither benefi cial for the patient nor in any way cost-effective (Miller 2002).

Nodules found at CT follow-up scans in patients with malignant disease have a higher likelihood to represent metastatic spread and require adequate therapeutic measures. The risk of a lung nodule to represent lung cancer is greater in the presence of particular underlying diseases (Kishi et al. 2002) or predisposing behavioral factors; however, there is an abundance of incidental nodular ﬁ ndings at chest CT in patients without known underlying malignancy, e.g. those undergoing CT angiography to exclude pulmonary embolism (Fig. 12.1). Lately, a plethora of indeterminate incidental ﬁ ndings in patients undergoing lung cancer screening aggra- vates this general phenomenon (Diederich et al.

2003; Garg et al. 2002; Li et al. 2002; Swensen 2002).

The clinicians, on the other hand, expect a ﬁ nal and hopefully correct diagnosis from the radiologist, which is hard to establish without invasive work- up (Mazzone and Stoller 2002) or with lacking information about actual and past clinical history (Zhang et al. 2002).

12.2 Classic Procedure for Work-Up

Some incidental indeterminate pulmonary nodules can be compared with previous scans or radiographs and classiﬁ ed as longstanding and unchanged;

however, classifying a nodule as “unchanged” can be treacherous ground. When evaluating nodules with diameters below 5 mm, a doubling in volume within 3 or 6 months, suggesting malignant growth, cannot be diagnosed by simply measuring the diameter of the lesion in two dimensions. This is due to the fact that the volume increases with the third power of the diameter. This is also the reason why a signiﬁ cant increase in volume can go along with only a minor increase in diameter, which often times cannot be measured on a 2D image, particularly in a thick-slice data set. Also, it is important to keep in mind that a nodule can grow unevenly in different dimensions. It can grow by ﬁ lling out little furrows or wrinkles on its surface, which would be challeng- ing to detect in a two-dimensional image, or it can grow only in the Z-direction, which would be missed when evaluating thick sections only.

Nodules can show distinct patterns of calcifi cations and thereby can be classifi ed as infl ammatory or post-specifi c (Fig. 12.2). Such nodules tend to be granulomatous in nature when histology is obtained (Hartman 2002).

If there are no previous examinations to compare or if the nodule is new, showing no signs of benignity, many clinical algorithms call for immediate invasive work-up of the lesion, such as CT-guided biopsy or

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176 P. Herzog et al.

even surgery (Fig. 12.3; Okajima et al. 2002; Wallace et al. 2002).

Marcus et al. in a re-evaluation of the initial Mayo Lung Project show that in a screening program in which every soft tissue nodule is evaluated by invasive means, the screening arm has no advantage regarding mortality or morbidity compared to a population in which no screening and invasive work- up of suspicious ﬁ ndings was performed (Marcus et

al. 2000). This is due to the low percentage of cancer in this population and the morbidity and even mortality incurred by the invasive procedures (Fig. 12.4;

Baldwin et al. 2002). Also direct surgical resection suffers from a certain rate of complications (Cooper 2002; Decamp 2002). Positron emission tomography scanning is probably useful in nodules >10 mm in diameter but expensive and less available than com- peting modalities (Fletcher 2002).

Fig. 12.1. Indeterminate pulmonary nodule in the left upper lobe of the lung in a patient with acute multiple pulmonary embolism as an incidental ﬁ nding. Subsequent work-up revealed small cell lung cancer

Fig. 12.2. Pulmonary nodule showing complete, diffuse calci- ﬁ cation as a sign of benignity, typical for post-inﬂ ammatory lesions

Fig. 12.3. Highly suspicious pulmonary nodule. Work-up with CT-guided biopsy. Histology reveals small cell lung cancer

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12.3 Advanced MDCT Strategies

Multi-slice CT (MSCT), with the ability of examining the whole thorax with thin slice sections in one breath hold aids us in the non-invasive evaluation of indeterminate pulmonary nodules. Therefore a collimation of 1 mm or less should be selected for scanning. Pitch can be increased up to 1.75, depending on the capabilities of the scanner and the number of detector rows. Scan time should not exceed 25 s for a comfortable breathhold time. If only the lung parenchyma is to be evaluated for pulmonary nodules, tube current and the resulting radiation dose can be drastically reduced compared to a staging CT of the thorax (Swensen et al. 2002). In such cases no contrast material needs to be administered. For appropriate evaluation of the mediastinum and the chest wall, standard radiation dose settings and intravenous contrast administration is needed. A tube voltage of 120 kV is appropriate for examining the thorax in most cases. In some cases (e.g. screening) a lower tube voltage of 100 or 80 kVp can be used to further decrease the radiation dose (Huda et al. 2002; Huda 2002). Reconstruction should be performed using a medium sharp lung kernel and an overlapping increment. Reconstructed slice thickness should be slightly greater than collimation to reduce noise and to smooth pitch artifacts. With recent generations of MSCT scanners this often times results in datasets of 500–600 axial images. A second reconstruction with a greater section thickness (e.g.

6 mm) can be performed to generate a second data set with a reasonable number of images suited for ﬁ lming or printing.

Reading should be performed on a workstation. To avoid reading of an excessive number of individual axial slices (500–600) image by image, a thin sliding MIP, MPR or VRT reconstruction can be used for more effective reading. Thin sliding MIPs have already proven to be the most suitable visualization method of all secondary reconstruction techniques for delineating small pulmonary nodules and distinguishing them from pulmonary vessels.

As mentioned above, typical patterns of calciﬁ - cation or fatty areas in a previously indeterminate nodule can be used as approximate markers for benignity or malignancy.

Thin slices better enable the reader to evaluate the internal structure of a nodule while thick slice thickness often obscures fatty or small calciﬁ ed areas due to partial-volume effect (Fig. 12.5); however, those subtle features are often an important sign or even proof of benignity, e.g. in the case of hamartomas.

Subtle features of benignity, comprising only a few hyperdense calciﬁ ed voxels or hypodense fatty areas, can also be evaluated and quantiﬁ ed by using a histogram analysis determining the number of voxels with this particular density versus the overall density of the lesion.

Histogram analysis is available on most types of PACS workstation viewing platforms.

Appropriate histogram analysis can only be performed based on thin-slice data because of the absence

Fig. 12.4. Indeterminate pulmonary nodule in a patient with emphysema, on the waiting-list for lung transplantation. Biopsy was required to rule out malignant disease as a contraindication for lung transplantation. Pneumothorax occurred as a com- plication of the invasive approach

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of considerable partial-volume effects. A histogram of a thick-slice data set of the same nodule would show only average densities around soft tissue values due to partial-volume effects. The best way to obtain such histograms is to apply the algorithm generating the histogram to a segmented 3D data set. When using segmented data, the histogram will contain only data from the nodule. Using 3D data ensures that the histogram contains data from the entire nodule and not only from individual slices in which relevant areas may not be included (Fig. 12.6). Generating segmented data sets and applying them to histogram analysis requires dedicated software that is not yet implemented in standard PACS environments.

The segmentation of 3D data sets is a mathematical process that divides the data set into areas with the same properties. When segmenting pulmonary nodules, each voxel of the data set is evaluated and classifi ed as being part of the nodule or not. In that process the gray-scale image data is transformed into a binary image (two-value image). The algorithm determines the borders (surface) of the nodule based on Houn- sfi eld densities of the individual voxels, the reason for which the segmentation process is based primarily on thresholding processes. A threshold of –200 HU has been proven to be most effi cient for the segmentation

of round pulmonary lesions. While a solid soft tissue mass has higher Hounsfi eld densities than –200 HU (most commonly around 70–80 HU if not calcifi ed), even with thin-slice isometric data a higher threshold would cause underestimation of the nodule size or shape due to partial-volume effects. This algorithm, of course, can only segment a nodule that has no contact with other non-nodular structures; therefore, the segmentation of lesions that are attached either to vessels, bronchi or the pleura is a considerable challenge for all segmentation algorithms. Such structures should be identifi ed and then separated from the nodule. One possible solution is to use an algorithm that aims at fi tting a spherical outline into each identifi ed structure and reducing its radius until both structures are separated. This method has the disadvantage of changing the number of voxels defi ning the nodule, thereby also changing the volume of the nodule. Another more suitable method is to use a morphological opening fi lter to smooth the surface of a nodule and to eliminate structures connected to its surface. This is a quite common method in digital image processing but has not yet found widespread use in the context of medi- cal imaging. Using mathematical operators, irregulari- ties of the contour are eliminated by erosion and the

“shrinking” of the volume is compensated by a ﬁ nal

Fig. 12.5. Indeterminate nodule on 10-mm slices above and 1-mm slices below: calci- ﬁ cation and fatty components allow ruling out malignancy when the nodule is inter- rogated using thin slices. Signs of benignity are obscured by volume averaging on thicker slices

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dilatation procedure. See Fig. 12.7 as an example using a 2D matrix.

Using this method as an iterative procedure, the surface is modulated until the lesion becomes a compact structure. The compactness factor is used to determine how compact a structure is. For a sphere the compactness factor (CF) is 1 or 100%, for a plane it is 0 or 0%.

Fig. 12.6. Histograms showing the number of voxels vs their Hounsfi eld density calculated based on segmented data: soft tissue nodule on the left and granuloma with tiny calcifi cations on the right. Calcifi cation of the granuloma not seen on axial images

If the CF exceeds a predetermined value, the iterative process is stopped and the contour can be saved for further evaluation.

This, potentially, is a simple, albeit-effective, way of distinguishing between nodules and surrounding structures, and to eliminate the latter. This way, most of the lesions attached to vessels, bronchi or the pleura can be separated correctly (Fig. 12.8). Other

Fig. 12.7. Morphological opening ﬁ lter for the smoothing of surfaces: ﬁ rst a layer of voxels (gray pixels) is eroded (Erosion).

Finally, a layer of the same number of voxels is added again to regain the original diameter of the lesion (Dilatation)

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a b

c d

e

Fig. 12.8a–e. Nodule segmentation resulting in an interactive 3D map. The red areas were recognized and segmented by the algorithm as the nodule of interest. Correct segmentation from a vessel (a) and from the chest wall/pleura and a feeding vessel (b). Failed segmentation from the chest wall (c), other nodules (d) and a vessel (e)

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algorithms have been developed and are already in the process of clinical evaluation that may further facilitate segmentation of pulmonary nodules from surrounding structures (Kido et al. 2002).

A nodule without fat or calcium as obvious signs of benignity still has to be considered as indeterminate because only a small portion of all incidental nodules are malignant and an invasive evaluation in every case would do more harm than good; therefore, other methods of non-invasive evaluation have to be considered.

12.4 Contrast Uptake

Contrast media uptake has been used for sensitive differentiation between benign and malignant lesions. Contrast media uptake can be determined by means of an actual perfusion study with multiple repetitive scans (up to 80 times) at the same table position encompassing the lesion. Another option is to perform an unenhanced scan before and four scans every minute after contrast enhancement. This way, more than one nodule can be evaluated with a total of fi ve scans without a further increase in radiation exposure. After those fi ve scans, the mean Hounsfi eld densities in the unenhanced scan are sub- tracted from the corresponding enhanced scans in all suspicious nodules. A threshold of enhancement of 15 HU has been found to be a sensitive (98%) but unspecifi c (55%) marker for differentiating between benign and malignant lesions.

12.5 CT Volumetry

Maybe the most efﬁ cient way to evaluate the dignity of an indeterminate nodule is to assess lesion growth.

Active malignancy should exhibit a certain growth pattern depending on the tissue type, especially when untreated. Small cell lung cancers have the highest growth rate with a doubling time of approximately 70 days. Lung cancers other than small cell lung cancer tend to have an intermediate growth rate with a doubling time of approximately100 days, whereas adenocarcinomas exhibit slower growth rate with a doubling time of approximately 130–160 days. Nod- ules with faster growth rates and doubling times of less than 50 days usually represent acute inﬂ amma-

tory lesions, which should recede under antimicro- bial therapy (Figs. 12.9, 12.10). Nodules with doubling times of more than 300–500 days usually represent benign or sub-acute inﬂ ammatory lesions when histology is obtained.

Based on thin-slice MDCT data, accurate CT volumetry can be performed based on automated segmentation algorithm as described above. Since growth is the very hallmark of malignancy, such tools may be the most suitable method for the non-invasive character- ization of lung lesions in the future and may help avoid unnecessary invasive procedures with the morbidity and mortality inherent to them (Fig. 12.11).

a

b Fig. 12.9. Squamous cell carcinoma. Initial scan on the upper and 3 months follow-up on the lower image panel. The lesion exhibits a malignant growth pattern with an increase from 160 mm³ to 260 mm³

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Fig. 12.11. Example of a dedicated software platform for visualization and analysis of focal lung disease: The LungCARE (Siemens, Erlangen, Germany) software platform enables intuitive visualization of focal lung disease using MIP, VRT or MPR reconstruc- tions in various imaging planes. If focal lung disease is found, accurate lesion volumetry can be performed, based of the principles described herein

a b

Fig. 12.10. Small cell carcinoma. Initial scan (37 mm³) on the left with 6 months follow-up (152 mm³) on the right

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